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9—
Trend Comparisons
Comparisons Between MSU and Radiosonde
Data Sets
One means of assessing the accuracy of the MSU data set is to
compare its results with those of other independent measurement
systems. Strictly speaking, it is impossible to compare time series
of global-mean tropospheric temperature anomalies based on
satellite data with radiosonde measurements, because the radiosonde
network does not provide global coverage (see the discussion in the
Radiosonde Observations chapter). To illustrate this point,
Figure 9.1 compares two global-mean tropospheric time series based
on the same MSU measurements. The red curve represents full global
coverage, while the pink curve is based on just a limited sampling
of grid points designed to mimic the existing distribution of
radiosonde stations. The least squares trend of the global coverage
time series is 0.06 ±0.11
°C/decade, compared with 0.14
±0.10 °C/decade for the sub-sampled time series.
The differences between these two curves indicate that the
radiosonde network may not be sufficiently dense to provide
reliable estimates of global-mean temperature anomalies.
Although one cannot compare MSU and radiosonde-based time series
of complete global-mean tropospheric temperature anomalies, it is
nonetheless possible to compare the sub-sampled MSU time series in
Figure 9.1 with the corresponding radiosonde-based time series.
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Representative terms from entire chapter:
temperature trends
Page 59
Figure 9.1
Global-mean seasonally averaged tropospheric time series based on the same
MSU 2LT measurements (Christy et al., 2000). The red curve represents full
global coverage, while the pink curve is based on a limited sampling of grid points
designed to mimic the existing distribution of radiosonde stations. The dark gray
curve (bottom) represents the difference between the sampled and full data sets.
The light gray lines represent the means of each time series. The first season is March–
May 1979 and the last season is December 1998–February 1999. The sub-sampled
MSU data were supplied by the U.K. Meteorological Office (UKMO).
principle, a close match constitutes an independent verification
of the MSU data. The two curves, shown in Figure 9.2, exhibit a
number of common features and rather similar trends.
Uncertainties exist in assigning confidence levels to trends
because of persistence in the data, which may or may not be due to
the trend itself. There is no unique set of confidence intervals
for the relatively short atmospheric temperature time series
considered here. The estimated confidence intervals depend on the
underlying statistical model that is used to describe the data, as
well as on the exact period considered and the sampling interval
(i.e., whether one uses monthly, seasonal, or annual means). One
approach, following Cryer (1986), yields trends of 0.14 ±0.10 °C/decade for the subsampled
MSU data and 0.04 ±0.07continue
Page 60
°C/decade for the radiosonde data (see Hurrell et al., in
review). Other approaches suggest even larger confidence intervals
(Santer et al., 2000).
Figure 9.2
Equivalent 'global-mean' MSU 2LT and radiosonde tropospheric time series.
The pink curve in this figure is identical to the pink curve in Figure 9.1 and represents
MSU data from those grid points from which long-term radiosonde observations are
available. The green curve in this figure is the average of the radiosonde data (Parker
et al., 1997) at the same grid points. The dark gray curve (bottom) represents the difference
between the MSU and radiosonde data sets shown in this figure. The axes are the same as in
Figure 9.1. Both data sets were supplied by the UKMO.
Some of the discrepancies between the MSU- and radiosonde-based
time series are a consequence of changes in radiosonde
instrumentation that have not been corrected. Christy et al. (2000)
performed a comparison analogous to the one shown in Figure 9.2,
but for a subset of 97 radiosonde stations operated by the United
States, whose records are believed to be free of such artificial
discontinuities for a specific period of time (Figure 9.3). The
level of agreement between the radiosonde data and the MSU data was
moderately improved by restricting the comparison to this more
limited selection of stations. High, middle, and low latitude
subsets of these stations (not shown) exhibited a comparably high
level of agreement with the MSU data. For the 97 stations as acontinue
Page 61
whole over the 16-year period from 1979 to 1994, the
station-by-station root mean squared (RMS) difference in monthly
anomalies averaged 0.45 °C (ranging from 0.18 to 0.78 °C),
and the RMS of station-by-station trend differences was 0.11
°C/decade. When averaged over the 97 stations, as well as into
the three regional subsets, the RMS of annual anomaly differences
ranged from 0.05 to 0.11 °C, and the trends were virtually
identical.
Figure 9.3.
Time series average of radiosonde data from 97 U.S.-operated stations, believed
to be free of artificial discontinuities (green curve). MSU 2LT data are from co-located
grid boxes (pink curve). The dark gray curve (bottom) represents the difference between
the MSU and radiosonde data sets shown in this figure. The axes are the same as in Figure
9.1, except that the data extend only through 1994.
Hurrell et al. (in review) and Santer et al. (2000) performed
similar subsampling studies, but over a considerably larger
geographical area. The work by Hurrell et al. compared the MSU data
to a somewhat different radiosonde data set that included more
radiosonde stations, but did not correct for some of the changes in
radiosonde instrumentation. Incontinue
Page 62
the temperate latitudes of North America, the RMS of grid-point
monthly anomaly differences ranged from 0.4 to 0.8 °C. In
addition, Hurrell et al. examined the impact of spatial averaging
methods on trends and concluded that given the spatial coherence of
the troposphere, especially in the tropics, the temperature
variations are mostly captured by the present sparse distribution
of stations if they are latitudinally averaged. Hurrell et al.
found that global trends for 1979–1998 were +0.06 (±0.11) °C/decade for MSU 2LT and
+0.04 (±0.07) °C/decade for
radiosondes. Santer et al. (2000) examined alternative methods of
computing global-scale trends in which spatial averaging procedures
and trend fitting methods were varied, giving results with larger
trend differences and error bars over shorter time scales.
Among the factors that need to be considered in accounting for
the differences between the results of Christy et al. (2000),
Hurrell et al. (in review), and Santer et al. (2000) are: (a)
differences in the size of the grid boxes over which radiosonde
data are averaged before comparing them with the satellite data,
(b) the different quality control criteria used for determining
which stations should and should not be included in the analysis,
(c) the use of daily versus monthly radiosonde reports, (d) the
treatment of missing data, and (e) how the emissions from the
surface that influence MSU values are handled. Further work will be
required to determine the relative importance of each of these
factors. In addition, given the multiplicity of decisions involved
in the design of algorithms for converting satellite radiances into
temperatures, the comparison of satellite and radiosonde trends
should be revisited following independent verification of these
data sets. Efforts to produce such data sets are already under
way.
Evidence Concerning Surface Versus
Tropospheric Temperature Trends
Figure 2.3 shows time series of surface and tropospheric
global-mean temperature anomalies. The former are based on surface
station and ship observations interpolated onto a global grid, and
the latter are based on the latest version (version D) of the MSU
data that have been corrected for the orbital decay problems
pointed out by Wentz and Schabel (1998). A difference in the trend
over this 20-year period is clearly apparent. Surface temperature
has been increasing at a rate ofcontinue
Page 63
about 0.1–0.2 °C/decade, whereas tropospheric
temperature has changed so little that a different sign for the
trend is obtained, depending on whether or not the final year of
the record is included—a year that was extraordinarily warm in
the wake of the exceptionally strong 1997–98 El Niño.
Spatial averages of surface and tropospheric temperature trends
over the tropics/extratropics, Northern Hemisphere/Southern
Hemisphere, and land/ocean exhibit qualitatively similar
differences.
Direct comparison of surface and tropospheric temperature
changes is feasible with radiosonde observations, because they
include both surface and upper-air data. For 1979–98, Angell
(1999) found the surface to have warmed more than the
mid-troposphere (850 to 300 hPa layer) globally, in qualitative
agreement with the results from the surface network and MSU.
However, the difference was not statistically significant because
of the large confidence intervals of the trends, due to the
relatively short data period and small radiosonde network used. In
selected high latitude regions in the Northern Hemisphere, Ross et
al. (1996) found evidence of decreasing temperature trends with
height from the surface through the troposphere for the period
1973–93. Using longer radiosonde data records extending back
approximately forty years, Angell (1999) found less pronounced (but
still noticeable) differences between surface and tropospheric
temperature trends than during the satellite period, consistent
with Jones's (1994) comparison of Angell's mid-tropospheric (850 to
300 hPa) radiosonde data with independent surface observations.
There is independent evidence that bears on the question of how
temperature at various levels of the tropical troposphere has
changed during the past 20 years. Even though the radiosonde data
do not appear to show evidence of a rise in the mean freezing level
in the tropics during this period (Gaffen et al., in review),
tropical glaciers in the Andes and in the high mountains of Africa
and Indonesia have retreated dramatically during this 20-year
period (Diaz and Graham, 1996; Thompson et al., 1995; Thompson,
1999). Another indication that tropospheric temperature has
increased is the fact that satellite and radiosonde measurements
indicate that the water vapor loading of the tropical troposphere
has increased (Wentz and Schabel, in press; Gaffen et al., 1992;
Gutzler, 1992, 1996).
Although the above findings are both suggestive of a warming of
the tropical troposphere, neither can be regarded as a definitive
indicator of how tropospheric temperature has changed during this
period. Thecontinue
Page 64
number of tropical glaciers is quite limited, and cloudiness,
precipitation, and wind speed, as well as temperature, could be
factors in their mass balance. With regard to the finding that
tropospheric water vapor is increasing, it should be noted that
most of the water vapor loading of the tropical atmosphere tends to
be concentrated within the lowest one to two kilometers of the
layer sampled by the MSU measurements, and may therefore be more
representative of surface temperature than tropospheric
temperature.
Figure 9.4.
Time series of average daily maximum (gray curve) and minimum (black curve)
land-surface air temperature anomalies, and their difference, the diurnal temperature
range (maximum minus minimum; dark gray curve at the bottom). This figure, which
encompasses the period 1950–99, illustrates a tendency toward a greater increase in
minimum than in maximum temperature. The annual anomalies are computed as differences
from the 1961–90 mean and use data from Peterson and Vose (1997).
Over many land areas, the range between daily maximum and
minimum temperatures (see Figure 9.4) has been decreasing in recent
decades, apparently largely in response to an increase in cloud
cover (Daicontinue
Page 65
et al., 1999). Daily maximum temperatures better reflect the
temperature of the air mass as a whole (i.e., the tropospheric
temperature), whereas the minimum temperatures often bear little
relation to temperatures aloft because they are strongly influenced
by the presence or absence of inversion layers close to the ground
(Dai et al., 1999; Hurrell et al., in review). The fact that daily
minimum temperatures have been rising at a more rapid rate than
daily maximum temperatures supports the notion that surface
temperature may actually be rising more rapidly than tropospheric
temperature.
Interpretation of the Differences
between Observed Surface and Tropospheric Temperature Trends
The warming trend of approximately 0.1–0.2 °C/decade
that has been observed at the earth's surface during the past 20
years clearly exceeds the observational uncertainties, including
the effects of urbanization (Hansen et al. 1995; Hurrell and
Trenberth, 1998). It is evident from Figure 6.2 that warming is
evident in both hemispheres, at most latitudes, over most of the
oceans, and over most land areas. It is also evident during all
seasons of the year.
There is more of a diversity of views among panel members with
respect to the degree of confidence that can be attached to the
absence of a warming trend in the MSU measurements. Those more
inclined to take the MSU measurements at face value cite the high
degree of consistency with radiosonde measurements (Figures 2.3,
9.2, and 9.3), whereas those less inclined to do so note the
retreat of the tropical glaciers and the increasing burden of water
vapor (Wentz and Schabel, in press). The seasonal and interannual
changes in the MSU temperature, sea surface temperature (see Figure
9.5), and atmospheric water vapor are closely coupled in the
tropics according to a relatively simple thermodynamic model (Wentz
and Schabel, in press). However, on decadal time scales, the trends
of sea surface temperature and water vapor continue to exhibit the
same close coupling, whereas the MSU and radiosonde temperature
trends are less correlated. Some panel members are concerned that
the satellite measurements may contain spurious discontinuities
resulting from changes in the times of day (i.e., diurnal sampling)
at which the satellites carrying the MSU instruments pass overhead
and the influence of radiation emitted from the underlying land
surface.break
Page 66
Figure 9.5.
Lower to mid-tropospheric temperature time series from MSU version C with orbital decay
corrections (C+O.D.) (aqua curve; Wentz and Schabel, 1998), version D (red curve; Christy
et al., 2000), and, for reference, sea surface temperature (black curve; updated from Reynolds
and Smith, 1994). The period of the NOAA-9 MSU observations is also indicated. The data are
seasonal averages over the tropics (20 °N to 20 °S). The MSU data are from the period 1979–1998,
and the SST data are from November 1981 through December 1998. The MSU data have been divided
by 1.6 to normalize the amplitude of the tropospheric temperature variations to that of the SST data.
Some of these concerns were recently addressed with a reissuing
of the MSU data set in a form in which adjustments have been made
to account for orbital changes, instrument heating, and changes in
diurnal sampling (see chapter 7). This adjusted version is referred
to as version D. Santer et al. (in review), Christy et al. (2000),
and Hurrell et al. (in review) have performed an analysis of the
differences between versions C (the previous version) and D. An
example of how the recent adjustments can affect the MSU
temperature trend is shown in Figure 9.5. Three anomaly time series
of the tropics are shown: MSU version C with the orbital decay
correction (C+O.D.) of Wentz and Schabel (1998), the more recent
MSU version D (Christy et al., 2000), and, for reference, the sea
surface temperature data set of Reynolds and Smith (1994). A
comparison of versions C+O.D. and D clearly shows a discrepancy
between the two versions for the periods of the NOAA-7, -9, and
-12continue
Page 67
satellites. During the NOAA-9 period, version C+O.D. more
closely tracks the SST data than version D, although version D
tracks the radiosondes more closely in this same period. As
mentioned above, the treatment of NOAA-9 was particularly
problematic due to a relatively small inter-satellite overlap
period and gain drift. In version D, a relatively large correction
for gain drift is applied to NOAA-9, equivalent to 0.17 °C over
two years, bringing its temperature data into closer agreement with
the observations of NOAA-6 and NOAA-8, which were also observing
during that period. However, the changes to the NOAA-7 and NOAA-12
data are actually greater and in the opposite direction than the
adjustments to the NOAA-9 data.
Part of the observed difference between global-mean trends in
surface temperature and tropospheric temperature may be a
reflection of the incomplete coverage of surface data, which are
sparse over the higher latitudes of the Southern Hemisphere. Recent
calculations of Santer et al. (in review), based on the
sub-sampling methodology described earlier, indicate that perhaps
as much as one-third of the difference may be due to this effect.
However, the panel views it as highly unlikely that incomplete
spatial coverage of the surface data could be the primary reason
for the disparity in the trends.
It seems more likely that at least part of the observed
disparity is a reflection of real differences between temperature
trends at the two levels. Temperatures near the earth's surface and
temperatures aloft are subject to different influences, and they
are often de-coupled from one another because of the presence of a
temperature inversion within the atmosphere's lowest 1–2 km
(Trenberth et al., 1992). Year-to-year variations in the
temperature of the tropics that occur in association with El
Niño tend to be about 30% greater in the middle troposphere
than at the earth's surface (Hurrell and Trenberth, 1998; Wentz and
Schabel, in press). In contrast, variations in. circulation over
the high northern latitudes exert a stronger influence on
global-mean temperature at the earth's surface than in the middle
troposphere (Hurrell and Trenberth, 1996). The emissions from
volcanic eruptions are believed to produce stronger cooling of the
troposphere than at the earth's surface (Bengtsson et al., 1999;
Hansen et al., 1997).
Within a given sampling interval like the past 20-years, any
changes in the temperature structure of the atmosphere that might
be occurring in response to a long-term increase in atmospheric
concentrations of greenhouse gases and aerosols may be masked by
internal variability ofcontinue
Page 68
the climate system (e.g., phenomena like El Niño), or
variability forced by volcanic eruptions, fluctuating solar
emissions, or even by short-term, naturally occurring variations in
greenhouse gas concentrations themselves. For example, if the
eruption of Mt. Pinatubo in 1991 were stronger and longer lasting
than that of El Chichon in 1982, this would have contributed to the
disparity between surface and tropospheric temperature trends of
the past two decades. The longer the period of record considered,
the stronger the likelihood that these naturally occurring
short-term fluctuations will average out so that the observed
trends are representative of the atmospheric response to longer
term trends in atmospheric composition.
The modeling evidence discussed in the following section
indicates that a 20-year record is subject to considerable
''sampling variability" due to the presence of the short-term
fluctuations in global-mean temperature discussed in the previous
paragraph. As further evidence, the panel notes that according to
the radiosonde record, the lower to mid troposphere warmed by a few
tenths of a degree C during the late 1970s (Jones, 1994; Santer et
al., 1999). Hence, the radiosonde record, to the extent that it can
be believed, serves to illustrate the sensitivity of the trends to
the particular choice of the period of record over which they are
computed, and it suggests that the apparent lack of agreement
between surface and upper air temperature trends during the past 20
years may not be representative of the longer term behavior of the
climate system.
Insights Derived from Model
Simulations
Much of our physical understanding of the climate system is
encapsulated in models. Climate models are capable of simulating
many of the processes that contribute to the observed differences
between variations in surface temperature and tropospheric
temperature, and they realistically represent the vertical
structure of El Niño-related temperature fluctuations and
the thermal signature of time-varying circulation patterns over
higher latitudes. Unlike the climate models of a decade ago, the
models used in recent simulations have enough horizontal resolution
and strong enough coupling with ocean and land to realistically
simulate not only the patterns of natural variability on
interannual time scales, but also the amplitudes of these internal
modes of variability of the climate system. Extended control runs
of suchcontinue
Page 69
models with no external forcing have been used as a basis for
estimating the likelihood that the natural variability of the
atmosphere-ocean system is responsible for the differing trend in
surface temperature and upper air temperature (Hansen et al., 1995;
Hurrell and Trenberth, 1998; Santer et al., in review). The models
indicate that natural variability may indeed have contributed to
the observed discrepancy, but unless the models are seriously
underestimating the natural variability, it is highly unlikely that
a differential trend as large as the one observed during the past
20 years could be entirely due to the internal variability of the
climate system.
Models provide one way to relate observed atmospheric changes to
those at the surface. This is typically done by forcing a climate
model with estimates of past changes in carbon dioxide, methane,
ozone, and other greenhouse gases, as well as solar variations and
volcanic and anthropogenic aerosols. The types of models used to do
this are either atmospheric general circulation models (GCM)
coupled to oceanic GCMs, or atmospheric GCMs driven by observed
SSTs. Both approaches have their relative advantages. Hansen et al.
(1993, 1997), and Bengtsson et al. (1999) have explored the former
approach. Bengtsson et al. note that the natural variability in
globally averaged temperature time series—which typically have
a standard deviation of 0.2 to 0.3 °C for a 20-year
interval—makes it difficult to establish long-term temperature
trends using a 20-year period. The coupled atmosphere-ocean GCM
simulations of Hansen et al. (1997) suggest that dynamic coupling
of the atmosphere and ocean tends to increase the variability of
the troposphere-surface temperature trend difference. These
studies, and those of Tett et al. (1996), highlight the importance
of including the decrease in stratospheric ozone during the past 20
years, as it substantially reduces upper tropospheric warming and
results in cooling in the lower stratosphere. In a series of fully
coupled ocean-atmosphere and uncoupled (specified SST) climate
model experiments with a range of natural and anthropogenic
forcings, Hansen et al. (1997) found that different forcing
mechanisms in the model simulations have different characteristic
vertical temperature signatures.
Some of the forcing combinations explored by Hansen et al.
(1997) yielded surface-troposphere trend differences similar to
those found in the observations. Folland et al. (1998) used an
atmospheric GCM forced by observed SSTs, greenhouse gases
(including stratospheric and tropospheric ozone), and tropospheric
sulfate aerosols for the period from 1950 to 1994, and similarly
noted much improved agreement withcontinue
Page 70
observed temperature profile changes. They were not, however,
able to reproduce the well-established strength of the observed
surface warming over land. These changes have been linked in part
to changes in atmospheric circulation (Hurrell, 1996), which have
to be simulated to correctly reproduce the observations. In
addition, it is quite possible that model-observation differences
arise as a result of errors in the greenhouse gas and aerosol
forcings that are applied. Thus, although climate models indicate
that changes in greenhouse gases (including tropospheric and
stratospheric ozone) and aerosols play a significant role in
defining the vertical structure of the observed atmospheric
temperature changes, model-observation discrepancies indicate that
the definitive model experiments have not yet been done.
To reduce model-based discrepancies, we need better information
on the changes in radiative forcings as a function of height,
especially tropospheric aerosols and ozone, as well as water vapor
changes and cloud changes caused by aerosols. In addition, it is
likely that further work will be needed to improve model
representations of the atmospheric boundary layer, clouds, and
vertical heat transport in order to fully represent the observed
temperature changes. Finally, models need to include more realistic
representation and coupling of the stratosphere, troposphere, and
ocean to fully capture the vertical structure of temperature
change.
Concluding Remarks
The various kinds of evidence examined by the panel led it to
conclude that the observed disparity between the surface and lower
to mid-tropospheric temperature trends during this particular
20-year period is probably at least partially real. Just as the
different factors that control daily maximum and minimum
temperatures are evidently giving rise to a trend in the diurnal
range of temperature, the different factors that control
temperatures at different levels of the atmosphere are capable of
altering the vertical profile of global-mean temperature. Human
induced forcings (e.g., increasing concentrations of well-mixed
greenhouse gases and stratospheric ozone depletion) give rise to
long term changes in the vertical profile, while natural forcings
such as volcanic eruptions and 'unforced' natural modes of climate
variability such as El Niño give rise to large year-to-year
changes which can alsocontinue
Page 71
contribute substantially to the trends observed during a period
of record as short as 20 years. The presence of these sampling
variations, together with the remaining uncertainties inherent in
the temperature measurements themselves, preclude the possibility
of drawing more definitive conclusions concerning the cause of the
observed disparity in the trends.
It is clear from the foregoing that reconciling the discrepancy
between the global-mean trends in temperature is not simply a
matter of deciding which one of them is correct, or determining the
ideal "compromise" between them. In the long term, it will require
major advances in the ability to interpret and model the subtle
variations in the vertical temperature profile of the lower
atmosphere that occur in association with the internal variability
of the climate system in response to volcanic eruptions and solar
forcing, and in connection with changes in atmospheric composition
due to human activities. It will also require more precise and
extensive satellite- and ground-based observations for monitoring
climate change, and changes in the way these observations are
implemented and processed. A detailed consideration of these issues
is beyond the scope of the panel's charge (see Preface). However,
the panel does offer a number of recommendations (see chapter 4)
for short-term actions that it views as steps in the right
direction.break